Connecting Heterogeneous Electronic Health Record Systems Using Tangle

Saweros, Emil; Song, Yeong-Tae

doi:10.1007/978-3-030-19063-7_68

Cited by 6 publications

(5 citation statements)

References 4 publications

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“…Throughout the analysis, the variables depend seasonally on environmental and clinical factors. For example, the number of asymptomatic cases and the vegetative index are time series, following [32]. Therefore, the research included the number of asymptomatic patients and their diagnoses.…”

Section: Resultsmentioning

confidence: 99%

“…The 21st century is an age of big data affecting every aspect of human life, including biology and medicine [30,31]. The move from paper medical records to Electronic Health Record (EHR) systems has resulted in an unprecedented increase in data [32,33]. Big data thus offers a great opportunity for doctors, epidemiologists, and specialists in health policy to make evidence-driven decisions that will eventually enhance patient care [34,35].…”

Section: Big Data In the Healthcare Systemmentioning

confidence: 99%

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Prediction of Malaria Fever Using Long-Short-Term Memory and Big Data

Awotunde

Jimoh

Oladipo

et al. 2021

Communications in Computer and Information Science

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Malaria has been identified to be one of the most common diseases with a great public health problem globally and it is caused by mosquitos' parasites. This prevails in developing nations where healthcare facilities are not enough for the patients. The technological advancement in medicine has resulted in the collection of huge volumes of data from various sources in different formats. A reliable and early parasite-based diagnosis, identification of symptoms, disease monitoring, and prescription are crucial to decreasing malaria occurrence in Nigeria. Hence, the use of deep and machine learning models is essentials to reduce the effect of malaria-endemic and for better predictive models. Therefore, this paper proposes a framework to predict malaria-endemic in Nigeria. To predict the malaria-endemic well, both environmental and clinical data were used using Kwara State as a case study. The study used a deep learning algorithm as a classifier for the proposed system. Three locations were selected from Irepodun Local Government Areas of Kwara State with 34 months periodic pattern. Each location reacted differently based on environmental factors. The findings indicate that both factors are significant in malaria prediction and transmission. The LSTM algorithm provides an efficient method for detecting situations of widespread malaria.

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Section: Resultsmentioning

confidence: 99%

Section: Big Data In the Healthcare Systemmentioning

confidence: 99%

Prediction of Malaria Fever Using Long-Short-Term Memory and Big Data

Awotunde

Jimoh

Oladipo

et al. 2021

Communications in Computer and Information Science

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“…The effectiveness of virtual care and health monitoring depends on the integrity of the EHRs that include a patient's medical history, diagnosis, medication, and treatment plans. The EHRs are highly sensitive and private information, which needs to be securely shared among peers, such as hospitals, pharmacies, and health regularity authorities to maintain a patient's medical data up to date [10] , [43] . The health legislation for telemedicine has empowered patients to control and manage their clinical data by setting data access and usage rules.…”

Section: Blockchain Opportunities In Telehealth and Telemedicinementioning

confidence: 99%

The role of blockchain technology in telehealth and telemedicine

Ahmad

Salah

Jayaraman

et al. 2021

International Journal of Medical Informatics

184

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“…The effectiveness of virtual care and health monitoring depends on the integrity of the EHRs that include a patient's medical history, diagnosis, medication, and treatment plans. The EHRs are highly sensitive and private information, which needs to be securely shared among peers, such as hospitals, pharmacies, and health regularity authorities to maintain a patient's medical data up to date [10,26]. The health legislation for telemedicine has empowered patients to control and manage their clinical data by setting data access and usage rules.…”

Section: A Patient Consent Managementmentioning

confidence: 99%

The Role of Blockchain Technology in Telehealth and Telemedicine

Ahmad¹,

Salah²,

Jayaraman³

et al. 2020

Preprint

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<div><b>Objectives: </b>Telehealth and telemedicine systems aim to deliver remote healthcare services to mitigate the spread of COVID‐19. Also, they can help to manage scarce healthcare resources to control the massive burden of COVID-19 patients in hospitals. However, a large portion of today's telehealth and telemedicine systems are centralized and fall short of providing necessary information security and privacy, operational transparency, health records immutability, and traceability to detect frauds related to patients' insurance claims and physician credentials.</div><div><b>Methods: </b>The current study has explored the potential opportunities and adaptability challenges for blockchain technology in telehealth and telemedicine sector. It has explored the key role that blockchain technology can play to provide necessary information security and privacy, operational transparency, health records immutability, and traceability to detect frauds related to patients' insurance claims and physician credentials.</div><div><b>Results: </b>Blockchain technology can improve telehealth and telemedicine services by offering remote healthcare services in a manner that is decentralized, tamper-proof, transparent, traceable, reliable, trustful, and secure. It enables health professionals to accurately identify frauds related to physician educational credentials and medical testing kits commonly used for home-based diagnosis.</div><div><b>Conclusions: </b>Wide deployment of blockchain in telehealth and telemedicine technology is still in its infancy. Several challenges and research problems need to be resolved to enable the widespread adoption of blockchain technology in telehealth and telemedicine systems.</div><div> </div><div><br></div>

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Connecting Heterogeneous Electronic Health Record Systems Using Tangle

Cited by 6 publications

References 4 publications

Prediction of Malaria Fever Using Long-Short-Term Memory and Big Data

Prediction of Malaria Fever Using Long-Short-Term Memory and Big Data

The role of blockchain technology in telehealth and telemedicine

The Role of Blockchain Technology in Telehealth and Telemedicine

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